general-fair-data-review

general-fair-data-review is a Peer review & grants skill for AI agents, published by learningmatter-mit in the AtomisticSkills repository on GitHub. Skillspace rates general-fair-data-review D (65/100) and it passed the safety scan; the rating is based on static analysis only.

Review a manuscript or code repository for FAIR data compliance (Findable, Accessible, Interoperable, Reusable), producing a structured report with pass/fail per principle and actionable remediation steps.

Grade
D
Quality
65 / 100
Safety
Passed
GitHub stars
132

Unproven: rated by static analysis only, not yet compared head-to-head.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

View source on GitHublearningmatter-mit/AtomisticSkills

Our review

Architecture
94 / 100
Maturity
Basic: a single SKILL.md
Security scan
No issues found
License
MIT
Adoption
Adopted by 0 other repositories, in 0 collections

Not checked yet

  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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